Generate A Custom Summary Using LLM Gateway

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import requests
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import time
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base_url = "https://api.assemblyai.com"
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headers = {"authorization": "<YOUR_API_KEY>"}
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# Step 1: Transcribe the audio
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audio_url = "https://storage.googleapis.com/aai-web-samples/meeting.mp4"
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data = {"audio_url": audio_url}
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response = requests.post(base_url + "/v2/transcript", json=data, headers=headers)
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transcript_id = response.json()['id']
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polling_endpoint = base_url + "/v2/transcript/" + transcript_id
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while True:
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    transcription_result = requests.get(polling_endpoint, headers=headers).json()
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    if transcription_result['status'] == 'completed':
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        break
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    elif transcription_result['status'] == 'error':
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        raise RuntimeError(f"Transcription failed: {transcription_result['error']}")
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    else:
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        time.sleep(3)
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# Step 2: Generate custom summary with LLM Gateway
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prompt = """
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- You are an expert at writing factual, useful summaries.
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- You focus on key details, leave out irrelevant information, and do not add in information that is not already present in the transcript.
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- Your summaries accurately represent the information in the transcript.
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- You are useful to the reader, are true and concise, and are written in perfect English.
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- Use multiple parts of the transcript to form your summary.
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- Make your summary follow the sequential order of events in the transcript.
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- Your summaries do not describe the context of the transcript - they only summarize the events in the text.
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- Your summaries do not describe what type of text they summarize.
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- You do not dumb down specific language nor make big generalizations.
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- Respond with just the summary and don't include a preamble or introduction.
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Your summary should use the following format: Bullet points
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"""
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llm_gateway_data = {
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    "model": "claude-sonnet-4-5-20250929",
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    "messages": [
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        {"role": "user", "content": f"{prompt}\n\nTranscript: {transcription_result['text']}"}
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    ],
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    "max_tokens": 1500
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}
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response = requests.post(
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    "https://llm-gateway.assemblyai.com/v1/chat/completions",
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    headers=headers,
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    json=llm_gateway_data
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)
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result = response.json()["choices"][0]["message"]["content"]
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print(result)

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import requests
2
import time
3
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base_url = "https://api.assemblyai.com"
5
headers = {"authorization": "<YOUR_API_KEY>"}
6
7
# Transcribe the audio
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audio_url = "https://storage.googleapis.com/aai-web-samples/meeting.mp4"
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data = {"audio_url": audio_url}
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response = requests.post(base_url + "/v2/transcript", json=data, headers=headers)
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transcript_id = response.json()['id']
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polling_endpoint = base_url + "/v2/transcript/" + transcript_id
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while True:
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    transcription_result = requests.get(polling_endpoint, headers=headers).json()
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    if transcription_result['status'] == 'completed':
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        break
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    elif transcription_result['status'] == 'error':
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        raise RuntimeError(f"Transcription failed: {transcription_result['error']}")
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    else:
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        time.sleep(3)

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prompt = """
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- You are an expert at writing factual, useful summaries.
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- You focus on key details, leave out irrelevant information, and do not add in information that is not already present in the transcript.
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- Your summaries accurately represent the information in the transcript.
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- You are useful to the reader, are true and concise, and are written in perfect English.
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- Use multiple parts of the transcript to form your summary.
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- Make your summary follow the sequential order of events in the transcript.
8
- Your summaries do not describe the context of the transcript - they only summarize the events in the text.
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- Your summaries do not describe what type of text they summarize.
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- You do not dumb down specific language nor make big generalizations.
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- Respond with just the summary and don't include a preamble or introduction.
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"""

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answer_format = "Bullet points"
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if answer_format:
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    prompt += f"\nYour summary should use the following format: {answer_format}"

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llm_gateway_data = {
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    "model": "claude-sonnet-4-5-20250929",
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    "messages": [
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        {"role": "user", "content": f"{prompt}\n\nTranscript: {transcription_result['text']}"}
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    ],
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    "max_tokens": 1500
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}
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response = requests.post(
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    "https://llm-gateway.assemblyai.com/v1/chat/completions",
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    headers=headers,
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    json=llm_gateway_data
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)
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result = response.json()["choices"][0]["message"]["content"]
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print(result)